"""simple docstring"""



import copy
import random

from transformers import CLIPTokenizer






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        super().__init__(*lowerCamelCase    ,		**lowerCamelCase						)
        __a       =    {}


 def 						a__   ( self    ,		lowerCamelCase    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    super().add_tokens(lowerCamelCase    ,		*lowerCamelCase    ,		**lowerCamelCase						)
        if num_added_tokens == 0:
               raise ValueError(
                   F"The tokenizer already contains the token {placeholder_token}. Please pass a different"
                   " `placeholder_token` that is not already in the tokenizer."						)


 def 						a__   ( self    ,		lowerCamelCase    ,		*lowerCamelCase    ,		lowerCamelCase=1    ,		**lowerCamelCase						):
        __a       =    []
        if num_vec_per_token == 1:
               self.try_adding_tokens(lowerCamelCase    ,		*lowerCamelCase    ,		**lowerCamelCase						)
               output.append(lowerCamelCase						)
        else:
               __a       =    []
               for i in range(lowerCamelCase						):
                      __a       =    placeholder_token + F"_{i}"
                      self.try_adding_tokens(lowerCamelCase    ,		*lowerCamelCase    ,		**lowerCamelCase						)
                      output.append(lowerCamelCase						)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
        for token in self.token_map:
               if token in placeholder_token:
                      raise ValueError(
                          F"The tokenizer already has placeholder token {token} that can get confused with"
                          F" {placeholder_token}keep placeholder tokens independent"						)
        __a       =    output


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=False    ,		lowerCamelCase=1.0						):
        if isinstance(lowerCamelCase    ,		lowerCamelCase						):
               __a       =    []
               for i in range(len(lowerCamelCase						)						):
                      output.append(self.replace_placeholder_tokens_in_text(text[i]    ,		vector_shuffle=lowerCamelCase						)						)
               return output
        for placeholder_token in self.token_map:
               if placeholder_token in text:
                      __a       =    self.token_map[placeholder_token]
                      __a       =    tokens[: 1 + int(len(lowerCamelCase						) * prop_tokens_to_load						)]
                      if vector_shuffle:
                             __a       =    copy.copy(lowerCamelCase						)
                             random.shuffle(lowerCamelCase						)
                      __a       =    text.replace(lowerCamelCase    ,		" ".join(lowerCamelCase						)						)
        return text


 def __call__( self    ,		lowerCamelCase    ,		*lowerCamelCase    ,		lowerCamelCase=False    ,		lowerCamelCase=1.0    ,		**lowerCamelCase						):
        return super().__call__(
            self.replace_placeholder_tokens_in_text(
                lowerCamelCase    ,		vector_shuffle=lowerCamelCase    ,		prop_tokens_to_load=lowerCamelCase						)    ,		*lowerCamelCase    ,		**lowerCamelCase    ,		)






 def 						a__   ( self    ,		lowerCamelCase    ,		*lowerCamelCase    ,		lowerCamelCase=False    ,		lowerCamelCase=1.0    ,		**lowerCamelCase						):
        return super().encode(
            self.replace_placeholder_tokens_in_text(
                lowerCamelCase    ,		vector_shuffle=lowerCamelCase    ,		prop_tokens_to_load=lowerCamelCase						)    ,		*lowerCamelCase    ,		**lowerCamelCase    ,		)

"""simple docstring"""



import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


SCREAMING_SNAKE_CASE__:List[str]									=			logging.getLogger(__name__)



def 	_lowerCamelCase(	):
       __a       =    argparse.ArgumentParser(
           description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."				)
       parser.add_argument(
           "--dataset_name"   ,    type=a   ,    default="wikitext"   ,    help="Name of the training. Explore datasets at: hf.co/datasets."   ,    )
       parser.add_argument(
           "--dataset_config"   ,    type=a   ,    default="wikitext-103-raw-v1"   ,    help="Configuration name of the dataset."				)
       parser.add_argument(
           "--tokenizer_name_or_path"   ,    type=a   ,    default="sayakpaul/unigram-tokenizer-wikitext"   ,    help="Tokenizer identifier. Can be a local filepath or a Hub identifier."   ,    )
       parser.add_argument(
           "--shard_size"   ,    type=a   ,    default=1_0_0_0   ,    help="Number of entries to go in a single shard."   ,    )
       parser.add_argument("--split"   ,    type=a   ,    default="train"   ,    choices=["train", "test", "validation"]				)
       parser.add_argument(
           "--limit"   ,    default=a   ,    type=a   ,    help="Limit the number of shards (used for debugging)."   ,    )
       parser.add_argument(
           "--max_length"   ,    type=a   ,    default=5_1_2   ,    help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
           " sequence length that is a multiple of 8."   ,    )
       parser.add_argument(
           "--output_dir"   ,    default="tf-tpu"   ,    type=a   ,    help="Output directory where the TFRecord shards will be saved. If the"
           " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
           " shards will be directly saved to a Google Cloud Storage bucket."   ,    )

       __a       =    parser.parse_args()
       return args



def 	_lowerCamelCase(	a				):
       def fn(a				):
              return tokenizer(examples["text"]				)

       return fn



def 	_lowerCamelCase(	a				):
       __a       =    []
       for i in range(len(tokenized_data["input_ids"]				)				):
              __a       =    {
                  "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]				)				),
                  "attention_mask": tf.train.Feature(
                      intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]				)				),
              }
              __a       =    tf.train.Features(feature=a				)
              __a       =    tf.train.Example(features=a				)
              __a       =    example.SerializeToString()
              records.append(a				)
       return records



def 	_lowerCamelCase(	a				):
       __a       =    datasets.load_dataset(args.dataset_name   ,    args.dataset_config   ,    split=args.split				)

       if args.limit is not None:
              __a       =    min(len(a				)   ,    args.limit				)
              __a       =    dataset.select(range(a				)				)
              print(F"Limiting the dataset to {args.limit} entries."				)

       __a       =    AutoTokenizer.from_pretrained(args.tokenizer_name_or_path				)

       # Handle output directory creation.
       # For serializing into a Google Cloud Storage Bucket, one needs to first
       # create a bucket.
       if "gs" not in args.output_dir:
              if not os.path.exists(args.output_dir				):
                     os.makedirs(args.output_dir				)
              __a       =    os.path.join(args.output_dir   ,    args.split				)
              if not os.path.exists(a				):
                     os.makedirs(a				)
       else:
              __a       =    os.path.join(args.output_dir   ,    args.split				)

       # Tokenize the whole dataset at once.
       __a       =    tokenize_function(a				)
       __a       =    dataset.map(a   ,    batched=a   ,    num_proc=4   ,    remove_columns=["text"]				)

       # We need to concatenate all our texts together, and then split the result
       # into chunks of a fixed size, which we will call block_size. To do this, we
       # will use the map method again, with the option batched=True. When we use batched=True,
       # the function we pass to map() will be passed multiple inputs at once, allowing us
       # to group them into more or fewer examples than we had in the input.
       # This allows us to create our new fixed-length samples. The advantage of this
       # method is that we don't lose a whole lot of content from the dataset compared to the
       # case where we simply tokenize with a pre-defined max_length.

       def group_texts(a				):
              # Concatenate all texts.
              __a       =    {k: sum(examples[k]   ,    []				) for k in examples.keys()}
              __a       =    len(concatenated_examples[list(examples.keys()				)[0]]				)
              # We drop the small remainder, though you could add padding instead if the model supports it
              # In this, as in all things, we advise you to follow your heart 🫀
              __a       =    (total_length // args.max_length) * args.max_length
              # Split by chunks of max_len.
              __a       =    {
                  k: [t[i : i + args.max_length] for i in range(0   ,    a   ,    args.max_length				)]
                  for k, t in concatenated_examples.items()
              }
              return result

       __a       =    dataset_tokenized.map(a   ,    batched=a   ,    batch_size=1_0_0_0   ,    num_proc=4				)

       __a       =    0
       __a       =    0
       for shard in range(0   ,    len(a				)   ,    args.shard_size				):
              __a       =    grouped_dataset[shard : shard + args.shard_size]
              __a       =    len(dataset_snapshot["input_ids"]				)
              __a       =    os.path.join(a   ,    F"dataset-{shard_count}-{records_containing}.tfrecord"				)
              __a       =    get_serialized_examples(a				)

              with tf.io.TFRecordWriter(a				) as out_file:
                     for i in range(len(a				)				):
                            __a       =    serialized_examples[i]
                            out_file.write(a				)
                     print("Wrote file {} containing {} records".format(a   ,    a				)				)

              shard_count += 1
              total_records += records_containing

       with open(F"split-{args.split}-records-count.txt"   ,    "w"				) as f:
              print(F"Total {args.split} records: {total_records}"   ,    file=a				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[Any]									=			parse_args()
   main(args)
